Systems and methods to evaluate application data

ABSTRACT

The present disclosure introduces systems and methods to evaluate application data. A computer system to evaluate application data is described. In one embodiment, a data module may be used to receive application data and validate the application data against at least one secondary data source. A predictive modeling module may be used to model the application data by applying predictive analytics. Further, a confidence level module may be used to calculate a confidence level factor and at least one aggregate degree of confidence level to evaluate an application. Other embodiments are also described.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority of U.S. Provisional PatentApplication Ser. No. 61/445,075, filed on Feb. 22, 2011, the content ofwhich is herein incorporated by reference.

BACKGROUND

1. Field of Invention

The present disclosure relates generally to systems and methods toevaluate application data, and more particularly to a predictiveanalysis process and system.

2. Description of the Related Art

There are many variables to consider when it comes to issuing insurancepolicies. Rate integrity can fluctuate greatly with all the differentvariables to evaluate. A one-size-fits-all approach may force insurancepolicyholders to pay much greater premiums than necessary to obtaincoverage. Inaccuracies in policy information can also result inadditional costs to both the insured and the insurer. Evaluating therisks associated with insuring an applicant and modeling data associatedwith that applicant can help an insurer obtain a more accuraterepresentation of its customers.

SUMMARY

The present disclosure introduces systems and methods to evaluateapplication data. A computer system to evaluate application data isdescribed. In one embodiment, a data module may be used to receiveapplication data and validate the application data against at least onesecondary data source. A predictive modeling module may be used to modelthe application data by applying predictive analytics. Further, aconfidence level module may be used to calculate a confidence levelfactor and at least one aggregate degree of confidence level to evaluatean application. Other embodiments are also described.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

An illustrative aspect of the invention relates to acomputer-implemented method for processing and evaluating applicationdata for an insurance policy in a remote computer processing system. Themethod including the steps of receiving application data relating to aninsurance policy and validating the application data against at leastone secondary data source. A predictive is determined by applyingpredictive analytics preferably using a plurality of variables todetermine the predictive and a confidence level is determined for eachof the plurality of variables. Contingent upon the predictive, aninsurance policy is issued if the aggregate confidence level is within apredetermined threshold, and if the aggregate confidence level is notwithin a predetermined threshold, issuance of the issuance policy isdenied.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments will now be described in detail with reference tothe accompanying drawings.

FIG. 1 depicts an exemplary computer system that may be used withillustrative embodiments of the present invention;

FIG. 2 is a block diagram illustrating a general overview of anapplication evaluation system, according to an example embodiment;

FIG. 3 is a block diagram illustrating the processing modules of thesystem shown in FIG. 2, according to an example embodiment;

FIG. 4 is a block diagram illustrating a method to evaluate an insuranceapplication, according to an example embodiment; and

FIG. 5 is a block diagram illustrating a method to assess anapplication, according to an example embodiment.

DETAILED DESCRIPTION

The present invention is now described more fully with reference to theaccompanying drawings, which illustrate various embodiments of thepresent invention. The present invention is not limited to any of theseillustrated embodiments since they are provided as merely exemplaryembodiments of the subject disclosure, which can be embodied in variousforms, as appreciated by one skilled in the art. Therefore, it is to beunderstood that any structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a basis for theclaims and as a representative for teaching one skilled in the art tovariously employ the present invention. Furthermore, the terms andphrases used herein are not intended to be limiting but rather toprovide an understandable description of the invention.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present invention, exemplarymethods and materials are now described. All publications mentionedherein are incorporated herein by reference to disclose and describe themethods and/or materials in connection with which the publications arecited.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an,” and “the” include plural referents unless thecontext clearly dictates otherwise. Thus, for example, reference to “astimulus” includes a plurality of such stimuli and reference to “thesignal” includes reference to one or more signals and equivalentsthereof known to those skilled in the art, and so forth.

The following detailed description is divided into several sections asfollows: an exemplary hardware and operating environment, a systemoverview, and methods of exemplary embodiments.

Exemplary Hardware and Operating Environment

This section provides an overview of one example of hardware and anoperating environment in conjunction with which embodiments of thepresent disclosure may be implemented. It is to be appreciated theembodiments of this disclosure, as discussed below, are preferably asoftware algorithm, program or code residing on computer usable mediumhaving control logic for enabling execution on a machine having acomputer processor. The machine typically includes memory storageconfigured to provide output from execution of the computer algorithm orprogram.

Turning now descriptively to the drawings, in which similar referencecharacters denote similar elements throughout the several views, FIG. 1depicts an exemplary general-purpose computing system in whichillustrated embodiments of the present invention may be implemented.

FIG. 1 depicts an exemplary computer system, i.e., system 100, which maybe used with an illustrative embodiment of the present invention. System100 generally includes at least one processor 115, or processing unit orplurality of processors, at least one interface 110, e.g., auser-interface, and a memory 125 coupled together via a bus or group ofbuses 112. Although system 100 is represented herein as a standalonedevice, it is not limited to such, but instead can be coupled to otherdevices (not shown) in a distributed processing system.

Interface 110 includes an input device, such as a keyboard or speechrecognition subsystem, for enabling a user to communicate informationand command selections to processor 115. Interface 110 also includes anoutput device such as a display. A cursor control such as a mouse,track-ball, or joy stick, allows the user to manipulate a cursor on thedisplay for communicating additional information and command selectionsto processor 115. Interface 110 is also provided for coupling theprocessing system 100 to one or more peripheral devices. Input tointerface 110 can be derived from multiple sources, for example keyboardinstructions in conjunction with data received via a network 120. Outputfrom interface 110 produces or generates output data and can comprise,for example, a display device or monitor in which case output data isvisual, a printer in which case output data is printed, a port forexample a USB port, a peripheral component adaptor, a data transmitteror antenna such as a modem or wireless network adaptor, etc. Output datacan also be distinct and derived from different output devices, forexample a visual display on a monitor in conjunction with datatransmitted to network 120. A user could view data output, or aninterpretation of the data output, on, for example, a monitor or using aprinter.

Processor 115 is an electronic device configured of logic circuitry thatresponds to and executes instructions. The processor 115 could comprisemore than one distinct processing device, for example to handledifferent functions within the processing system 100. Processor 115outputs, to user interface 110, a result of an execution of the methodsdescribed herein. Alternatively, processor 115 could direct the outputto a remote device (not shown) via network 120.

Memory 125 is a computer-readable medium encoded with a computerprogram. In this regard, memory 125 stores data and instructions thatare readable and executable by processor 115 for controlling theoperation of processor 115. Memory 125 may be implemented in a randomaccess memory (RAM), volatile or non-volatile memory, solid statestorage devices, magnetic devices, a hard drive, a read only memory(ROM), or a combination thereof. One of the components of memory 125 isa program module 130.

Program module 130 contains instructions for controlling processor 115to execute the methods described herein. Examples of these methods areexplained in further detail in the subsequent of exemplary embodimentssection-below. The term “module” is used herein to denote a functionaloperation that may be embodied either as a stand-alone component or asan integrated configuration of a plurality of subordinate components.Thus, program module 130 may be implemented as a single module or as aplurality of modules that operate in cooperation with one another.Moreover, although program module 130 is described herein as beinginstalled in memory 125, and therefore being implemented in software, itcould be implemented in any of hardware (e.g., electronic circuitry),firmware, software, or a combination thereof.

In use, system 100 is adapted to allow data or information to be storedin and/or retrieved from, via wired or wireless communication, at leastone database (not shown). Processor interface 110 can provide wiredand/or wireless communication the processor 115 and peripheralcomponents that may serve a specialized purpose. Preferably, processor115, under the control of instructions from program module 130, receivesinput data via network 120 or interface 110. Processor 115 furtherprocesses this input data to yield resultant processed data. Processor115 further provides the processed data as output data to interface 110or to network 120 for further transmission. It should be appreciatedthat the processing system 100 may be any form of terminal, server,specialized hardware, or the like.

It is to be further appreciated that network 120 depicted in FIG. 1 caninclude a local area network (LAN) and a wide area network (WAN), butmay also include other networks such as a personal area network (PAN).Such networking environments are commonplace in offices, enterprise-widecomputer networks, intranets, and the Internet. For instance, when usedin a LAN networking environment, the computing system environment 100 isconnected to the LAN through a network interface or adapter (not shown).When used in a WAN networking environment, the computing systemenvironment typically includes a modem or other means for establishingcommunications over the WAN, such as the Internet. The modem, which maybe internal or external, may be connected to a system bus via a userinput interface, or via another appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computing systemenvironment 100, or portions thereof, may be stored in a remote memorystorage device such as storage medium 135. It is to be appreciated thatthe illustrated network connections of FIG. 1 are exemplary and othermeans of establishing a communications link between multiple computersmay be used.

FIG. 1 is intended to provide a brief, general description of anillustrative and/or suitable exemplary environment in which embodimentsof the below described present disclosure may be implemented. FIG. 1 isan example of a suitable environment and is not intended to suggest anylimitation as to the structure, scope of use, or functionality of anembodiment of the present invention. A particular environment should notbe interpreted as having any dependency or requirement relating to anyone or combination of components illustrated in an exemplary operatingenvironment. For example, in certain instances, one or more elements ofan environment may be deemed not necessary and omitted. In otherinstances, one or more other elements may be deemed necessary and added.

In the description that follows, certain embodiments may be describedwith reference to acts and symbolic representations of operations thatare performed by one or more computing devices, such as the computingsystem environment 100 of FIG. 1. As such, it will be understood thatsuch acts and operations, which are at times referred to as beingcomputer-executed, include the manipulation by the processor of thecomputer of electrical signals representing data in a structured form.This manipulation transforms the data or maintains them at locations inthe memory system of the computer, which reconfigures or otherwisealters the operation of the computer in a manner understood by thoseskilled in the art. The data structures in which data is maintained arephysical locations of the memory that have particular properties definedby the format of the data. However, while an embodiment is beingdescribed in the foregoing context, it is not meant to be limiting asthose of skill in the art will appreciate that the acts and operationsdescribed hereinafter may also be implemented in hardware.

Embodiments may be implemented with numerous other general-purpose orspecial-purpose computing devices and computing system environments orconfigurations. Examples of well-known computing systems, environments,and configurations that may be suitable for use with an embodimentinclude, but are not limited to, personal computers, handheld (e.g.,smart phones, tablet devices, etc.) or laptop devices, personal digitalassistants, multiprocessor systems, microprocessor-based systems, settop boxes, programmable consumer electronics, network, minicomputers,server computers, game server computers, web server computers, mainframecomputers, and distributed computing environments that include any ofthe above systems or devices.

Embodiments may be described in a general context of computer-executableinstructions, such as program modules, being executed by a computer.Generally, program modules include routines, programs, objects,components, data structures, etc., that perform particular tasks orimplement particular abstract data types. An embodiment may also bepracticed in a distributed computing environment where tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules may be located in both local and remote computer storage mediaincluding memory storage devices.

As used herein, the term “software” is meant to be synonymous with anycode or program that can be in a processor of a host computer,regardless of whether the implementation is in hardware, firmware or asa software computer product available on a disc, a memory storagedevice, or for download from a remote machine. The embodiments describedherein include such software to implement the equations, relationshipsand algorithms described above. One skilled in the art will appreciatefurther features and advantages of the invention based on theabove-described embodiments. Accordingly, this disclosure is not to belimited by what has been particularly shown and described, except asindicated by the appended claims.

System Level Overview

FIG. 2 comprises a block diagram illustrating a general overview of anapplication evaluation system 200 according to an example embodiment.Generally, the application evaluation system 200 may be used to obtainand evaluate application data. It is to be appreciated, in the belowillustrative description, the application evaluation system 200 is usedto evaluate application data regarding issuance, and ratedeterminations, for an issuance policy. It is to be appreciated theinvention is not to be understood to be limited to such an illustrativeembodiment as it may be adapted and for configured for many applicationshaving application data/information, such as, for example, anapplication for a line of credit for either secured or unsecured assets.

In this exemplary implementation, the application evaluation system 200comprises inputs 202, computer program processing modules 204, andoutputs 206. In one embodiment, the application evaluation system 200may be a computer system such as shown in FIG. 1, discussed-above.Inputs 202 are received by processing modules 202 and processed intooutputs 206. Inputs 202 may include internal/external data, riskvariables, rate variables, and predictive variables. Inputs 202 may alsoinclude policy account information. A plurality of each type of variablemay be inputted into the application evaluation system 200.

A first input 202 may be internal/external data. Internal/external datamay be any data captured, received, transmitted, or processed during theapplication evaluation process. Internal data may be any informationuseful to the decision making process found within an organizationconducting an application review. Internal/external data may be used toauthenticate customer application data. Internal data may includecustomer supplied information, loss information, and driving experienceinformation. In an example embodiment, internal data may be datacollected by an organization such as operating or customer data. Anexample of an organization that may utilize internal data includes, butis not limited to, an insurance provider, or an organization thatprovides a line of credit for secured and unsecured credit. An insuranceprovider may receive application data pertaining to a customer seekingto become insured. Internal data may also include historical data, whichthe organization maintains pertaining to an existing or potentialcustomer. In one embodiment, internal data in the form of historicaldata may have been accumulated by the organization from past dealings ortransactions with the customer.

External data may be any information obtained by an organization througha third-party data source. External data may be collected by a thirdparty and received by an organization. Examples of external data mayinclude but are not limited to third-party data, public records, priorinsurance records, and internet resources and accessible sources, amongothers. In one embodiment, an organization may purchase proprietaryinformation from a third-party source to improve the applicationevaluation process within its organization.

Internal/external data may be obtained by a variety of communicationchannels. Communication channels may include, but are not limited to,internet, networks, mobile devices, phones, satellites, and in-personconsultation, among others. However the internal/external data may becaptured, it will be transformed by the processing modules 204 to createoutputs 206.

Another input 202 may be risk variables. Risk variables may beattributes that identify the potential risk for an insured exposure toincur future loss or profitability. Risk variables can be anyinformation used to evaluate the short-term risk of insuring a customeras well as the long-term risk of insuring that same customer. In oneembodiment, risk variables may be used to verify application dataprovided to an organization. Specific examples of risk variables can befound in the “Example Implementations” section of this specification.

Yet another input 202 may be rate variables. Rate variables may be anyconsiderations that can affect the rate charged by an organization to acustomer. Rate variables may be attributes that provide for accuratepricing of an exposure to be insured. Rate variables may dovetail withrisk variables. One example of this may be the location/address of acustomer. If the customer is located in a high crime area, there is agreater risk that harm can be caused to person/property and a higherrate may likely be charged to insure the customer. Specific examples ofrate variables can be found in the “Example Implementations” section ofthis specification.

Another input 202 may include predictive variables. Predictive variablesmay be analytical attributes that can be used to display the potentialfor future loss or profitability of a potential customer. Predictivevariables may aim to address the long-term insurability of a customer.Predictive variables may coincide with both risk and rate variables. Oneexample of an attribute that may be considered all three types is themilitary status of a customer. Specific examples of predictive variablescan be found in the “Example Implementations” section of thisspecification.

Processing modules 204 generally include routines, computer programs,objects, components, data structures, etc., that perform particularfunctions or implement particular abstract data types. The processingmodules 204 receive inputs 202 and apply the inputs 202 to capture andprocess application data producing outputs 206. The processing modulemay evaluate both internal and external outputs 206 to determinepossible interactions within individual data values. The processingmodules 204 are described in more detail by reference to FIG. 3.

The outputs 206 may include degree of confidence factors to evaluate thevariables of an application, an aggregate confidence level to evaluatean application in its entirety, and an insurance policy quote/policyissuance. In one embodiment, inputs 202 are received by processingmodules 204 and applied to application data to create confidence in andauthenticate application data and the variables which may affectinsuring a customer and issuing that customer an insurance policy. Theoutputs 206 measure confidence of prediction output based on reliabilityof data. The outputs 206 may include a weighted score of individual dataelements based on input source credibility and relevance.

One output 206 may be degree of confidence factors. Degree of confidencefactors may be used to verify the accuracy of application data as wellas to indicate the reliability of an estimate of each input variable. Inone embodiment, degree of confidence factors may be used to validate theaccuracy of application data obtained by an organization. Applicationdata obtained by an organization may be modeled and potential conflictsmay be identified and investigated. The output 206 may be used tosuggest a value or a range of values for an individual variableindicating what the expected value of the variable.

In another embodiment, degree of confidence factors may be used toestimate the reliability of input variables. A degree of confidencefactor may be created for each individual variable used to evaluate anapplication. In one embodiment, certain input variables may bedetermined to be more critical than others to accurately evaluate anapplication. An organization may consider some variables more importantthan others. The organization may make a determination on whichvariables are deemed to be important to evaluating an application. Forexample, variables such as mileage driven on a daily basis as well asdriving record may be more relevant when evaluating an automobileinsurance policy than a homeowner's policy. In different applications,different variables may be assigned greater weight or deference. Theoutput 206 may include a weighted score of individual data elementsbased on source credibility and relevance.

A second output 206 may be an aggregate confidence level used toevaluate an application in its entirety. The aggregate confidence levelmay be a combination of the evaluation of a majority or all inputvariables. In one embodiment, a single aggregate confidence level may becreated. In another embodiment, multiple aggregate confidence levels maybe produced. For instance, an aggregate confidence level may be createdfor all risk variables, rate variables, or predictive variablesassociated with the evaluation of an application. Both the degree ofconfidence factors and the aggregate confidence level may be calculatedby applying statistical analysis to the input data 202. Statisticalanalysis may involve evaluation of degrees of variance betweenapplication and transaction data elements and predictive outputs todetermine whether a conflict exists and create an individual variableand an aggregate score.

Yet another output 206 may be insurance policy quote/policy issuance.The insurance policy quote/policy issuance may be directly related tothe degree of confidence factors and the aggregate confidence leveldetermined by modeling and processing the inputs 202. In one embodiment,a threshold value may be determined and compared to the confidenceassessments and applied business rules to make a determination if aninsurance policy will be issued to a customer. This assessment may alsoprovide a rate quote that may be provided to a customer showing the costassociated with obtaining an insurance policy. In another embodiment,the processed input data and applied business rules may also be used toreevaluate the rate and policy of an existing insurance policy. Thecombination of the confidence, conflict assessment, and business ruleswill determine the decision outputs: accept, refer, decline, modify, orvalidate.

For illustration, consider an applicant named “Bob.” In the process ofapplying for insurance, Bob provides his name, social security number,and date of birth. It is understood that an applicant may be a personwho already has insurance. Thus, the term “applicant” may refer to aninsured person looking to apply for new or different insurance (e.g., arenewal or replacement policy), or an uninsured person looking to applyfor new insurance (e.g., a new policy). Each of these pieces of data maybe used as inputs representing risk, e.g., risk variables. For example,Bob's name and date of birth may be verified for accuracy usingthird-party data. If an inconsistency is found, the risk of insuring Bobis presumed to be higher, unless some reasonable explanation isprovided. Similarly, Bob's social security number may be validated andused to assess risk. In addition, Bob's social security number and dateof birth may be used to calculate rate, e.g., rate variables. Bob's dateof birth may be used to determine age, which may have an effect on Bob'srate. Additionally, Bob's social security number may be used to obtain acriminal background check and determine whether Bob has any criminalhistory. Bob's criminal history may be useful as a predictive variable.For example, the type of offenses, timing between offensive, and age ofthe most recent offense, may be useful to determine whether Bob iscreditworthy.

If any of Bob's information is considered inaccurate or untrustworthy,Bob may be denied coverage. Alternatively, a customer servicerepresentative may contact Bob to discuss the unconfirmed informationand ask for proof or additional information. The action taken inresponse to Bob entering information may be conditional upon theconfidence level of the inaccuracy. For example, depending on the sourceof third-party data, the confidence in such data may vary. Thus, whenBob's date of birth is validated against third-party data, theconfidence in the data may affect the resulting action if Bob's date ofbirth is questionable. If the confidence in the third-party data ishigh, then Bob may be denied coverage under the belief that Bob isattempting to defraud the insurance company. However, if the confidencein the third-party data is low, Bob may be contacted to verify his dateof birth or correct the information he submitted.

FIG. 3 is a block diagram of the processing modules 204 of the system200 shown in FIG. 2, according to various embodiments. Processingmodules 204, for example, comprise a data module 302, a predictivemodeling module 304, and a confidence level module 306. Alternativeembodiments are also described below.

The first module, a data module 302, may be used to receive applicationdata and validate the application data against at least one secondarydata source. Application data may be received from a variety ofdifferent communication channels, such as the communication channelsnoted in the description of FIG. 2. In one embodiment, commoncommunication channels to receive application data includeinternet/email, mobile device, phone, and in-person consultation.Application data may be entered by a customer and received by anorganization. Alternatively, application data may be obtained by anorganization. In one embodiment, an organization may be an insuranceprovider entering application data on behalf of the customer.

Once the application data is received, the data module 302 may also beused to validate the application data by comparing it to at least onesecondary data source. A secondary data source may be any source of dataother than the customer or applicant who submitted the originalapplication data. The secondary data source may be used to both validatethe application data entered on behalf of a customer as well as providesadditional information to supplement the application process. In oneembodiment, the secondary data source may be historical data about thecustomer/applicant provided by an organization. An organization such asan insurance provider may have historical data such as insurance recordsor history on file. In an alternative embodiment, a secondary datasource may be third-party data. Third-party data may be any informationobtained that is not provided by the organization receiving theapplication data. In an example embodiment, an organization such as aninsurance provider may purchase data from a third-party source.

The second module, a predictive modeling module 304, may be used tomodel the application data. The predictive modeling module 304 may applypredictive analytics to analyze the application data and data acquiredfrom the at least one secondary source. Additionally, the predictivemodeling module 304 may use a plurality of variables to further modelthe application data and data acquired from the at least one secondarysource. Predictive analytics may be used to extract information fromboth the application data and secondary source data, and use that datato predict future trends and behavior patterns. The predictive analyticswill measure specific interactions between individual data among allinternal and external data inputs. The plurality of variables used bythe predictive modeling module 304 should bear a relationship to pastdata and exploit such data to predict future outcomes. Furthermore,including the plurality of variables in the modeling process may allowan organization to accurately evaluate an application in both theshort-term and the long-term. In one embodiment, the plurality ofvariables may include risk variables, rate variables, and predictivevariables, such as those described in the description of FIG. 2.Specific examples of the plurality of variables used by the predictivemodeling module 304 can be found in the “Example Implementations”section of this specification.

The third module, a confidence level module 306, may be used tocalculate a confidence level factor for each of the plurality ofvariables. Additionally, the confidence level module 306 may create atleast one aggregate degree of confidence level to evaluate anapplication. Confidence level factors may be used to estimate thereliability of the plurality of variables modeled by the predictivemodeling module 304. A confidence level factor may be created for eachindividual variable used to evaluate an application.

Moreover, the confidence level module 306 may be used to create at leastone aggregate degree of confidence to evaluate an application. Theaggregate degree of confidence may comprise a combination of theconfidence level factors created for the plurality of variables.Additionally, the aggregate degree of confidence may take into accountthe application data and data obtained from secondary data sources toprovide a more accurate evaluation of the application. In oneembodiment, a single aggregate confidence level may be created. Inanother embodiment, multiple aggregate confidence levels may beproduced. In an example embodiment, an organization such as an insuranceprovider will utilize both the degree of confidence factors and theaggregate confidence level to evaluate an insurance application, gainingboth short-term and long-term perspectives.

In an example embodiment, the confidence level module 306 may furtherinclude weighting certain individual variables from the plurality ofvariables to improve the accuracy of the aggregate degree of confidencelevel determination. As described in the description of FIG. 2, anorganization may consider some variables more important than othervariables. The organization may make a determination on which variablesare deemed interesting when evaluating an application. In an exampleembodiment, the confidence level module 306 may further be used todetermine a threshold confidence level to issue an insurance policy.

In an alternative embodiment, an additional processing module, namely, apolicy module 308 may be used to provide rate quotes and issue insurancepolicies. Additionally, the policy module 308 may also be used tore-evaluate existing insurance policies using the other processingmodules 204 described.

In another alternative embodiment, yet another processing module, namelya conflict check module 310 may be used to flag conflicts in theapplication data. Conflicts will be determined based on the degree ofvariance between application and transaction data and the predictivelevel outputs. Moreover, the conflict check module 310 may be used by anorganization to determine follow-up measures to address detectedconflicts in the application data.

Exemplary Methods

In this section, particular methods to evaluate application data andexample embodiments are described by reference to a series of flowcharts. The methods to be performed may constitute computer programsmade up of computer-executable instructions.

FIG. 4 is a block diagram illustrating a method 400 to evaluate aninsurance application, according to an example embodiment. The method400 represents one embodiment of an application evaluation system suchas the application evaluation system 200 described in FIGS. 1 and 2respectively. The method 400 may be implemented by obtaining applicantdata (block 402), modeling a plurality of risk and rate variables tocreate degree of confidence factors (block 404), and calculating atleast one aggregate degree of confidence level to insure an applicant(block 406).

Applicant data is obtained at block 402. As discussed above, withrespect to FIG. 2, applicant data can be internal/external which isincludes any data captured received, transmitted or processed during theapplication evaluation process. It is also understood that an applicantmay be a person who already has insurance. Thus, the term “applicant”may refer to an insured person looking to apply for new or differentinsurance (e.g., a renewal or replacement policy), or an uninsuredperson looking to apply for new insurance (e.g., a new policy).Applicant data may be received from a variety of different communicationchannels, such as the communication channels noted in the description ofFIG. 2. Applicant data may be entered by a customer and received by anorganization. Alternatively, applicant data may be obtained by anorganization. In one embodiment, an organization may be an insuranceprovider entering applicant data on behalf of the customer.

A plurality of risk and rate variables is modeled at block 404 to createdegree of confidence factors. Modeling may include comparing theapplicant data to at least one data set in order to create a degree ofconfidence factor for each of the plurality of risk and rate variables.Modeling may further include applying predictive analytics thatincorporate the applicant data and at least one data set. In oneembodiment, predictive analytics may be performed on a computer devicehaving a processor so that statistical analysis can be applied.

A data set may be any collection of information that may be pertinent toevaluating an insurance application. The data set may be obtained from avariety of different communication channels, such as the communicationchannels noted in the description of FIG. 2. In one embodiment, a dataset may be manipulated by a computer. In an example embodiment, a dataset may be historical data specific to the applicant. In another exampleembodiment, a data set may be third-party data. Block 404 may furtherinclude flagging conflicts in the application data when compared to theat least one data set.

The risk and rate variables used at block 404 may be associated with theapplicant data obtained at block 402. Specific examples of the pluralityof risk and rate variables modeled can be found in the “ExampleImplementations” section of this specification. Confidence level factorsmay be used to estimate the reliability of the plurality of risk andrate variables modeled. A confidence level factor may be created foreach individual variable used to evaluate an application.

At least one aggregate degree of confidence level is calculated at block406. The aggregate degree of confidence level may comprise a combinationof the confidence level factors created for the plurality of risk andrate variables. Additionally, the aggregate degree of confidence levelmay take into account the applicant data and data obtained from at leastone data set to provide a more accurate evaluation of the application.In one embodiment, a single aggregate degree of confidence level may becreated. In another embodiment, multiple aggregate degrees of confidencelevels may be produced. In an example embodiment, block 406 may furtherinclude determining which individual risk and rate variables are mostcritical to insuring the applicant. In another example embodiment, block406 may further include weighting the determined most critical risk andrate variables in the calculation of the aggregate degree of confidencelevel.

An alternative embodiment of FIG. 4 may further include determining athreshold confidence level needed to issue an insurance policy for theapplicant (block 408). The threshold confidence level may be compared tothe confidence level factors and the at least one aggregate degree ofconfidence level to evaluate whether an application should be issued.Another alternative embodiment to FIG. 4 may further include providing arate quote to insure the applicant (block 310). Additionally, yetanother alternative embodiment of FIG. 4 may include issuing aninsurance policy on behalf of an applicant (block 412).

FIG. 5 is a block diagram illustrating a method 500 to assess anapplication. The method 500 represents one embodiment of an applicationevaluation system such as the application evaluation system 200described in FIGS. 1 and 2, respectively. The method 500 may beimplemented by determining a number of conflicts in the providedapplicant data (block 502), and calculating at least one level ofconfidence factor (block 504).

A number of conflicts in the provided applicant data are determined atblock 502. Conflicts in the provided applicant data may be determined bycomparing the provided applicant data to the historical data. Providedapplicant data refers to applicant data as previously describedthroughout this description. Historical data may be any informationpertaining to an existing or potential customer/applicant. In oneembodiment, historical data may have been accumulated by theorganization from past dealings or transactions with the customer.Conflicts identified in the provided applicant data may be flagged forreview.

At least one level of confidence factor for the accuracy of the providedapplicant data is calculated at block 504. The level of confidencefactor is created by applying predictive analytics to the providedapplicant data and the historical data about the applicant. In oneembodiment, multiple levels of confidence factors may be calculated toincrease the accuracy of the evaluation of the provided applicant data.

An alternative embodiment to FIG. 5 may further include determining athreshold confidence level needed to issue an insurance policy (block506). The threshold confidence level may be compared to the at least onelevel of confidence factor to evaluate an application. In oneembodiment, the threshold confidence level of block 506 may bedetermined by an organization such as an insurance provider.

Another alternative embodiment to FIG. 5 may include comparing theprovided applicant data to third-party data (block 508). This may occurafter determining the number of conflicts in the provided applicant data(block 502). Third-party data may be any information obtained that isnot provided by the organization receiving the provided applicant data.In an example embodiment, an organization such as an insurance providermay purchase data from a third-party source. If third-party data is usedto validate the accuracy of the provided applicant data, block 504 mayfurther comprise utilizing the third-party data in the predictiveanalytics analysis to improve the accuracy of the at least one level ofconfidence factor.

Exemplary Implementations

Various examples of computer systems and methods for embodiments of thepresent disclosure have been described above. Listed and explained beloware alternative embodiments, which may be utilized by the applicationevaluation system. Specifically, a listing of a plurality of variablesthat may be used to evaluate application data is provided. The listingbelow enumerates a number of risk, rate, and predictive variables. Itshould be noted that this listing is intended to provide examples ofpotential variables that may be used. It is not intended to be anexhaustive list of all potential variables that may be applied toevaluate application data.

Potential Risk Variables can include, but are not limited to: Names;Social Security Number (“SSN”); Date of Birth (“DOB”); Military Status;Criminal Convictions; Business or Commercial Address; Title History ofVehicle; Indicators of Prior Damage; Validation of Commercial orBusiness Use; Vehicle Registration; and Capture of All Licensed Driversin a Household.

Potential Rate Variables can include, but are not limited to: SSN; DOB;Military Status; Location of Employment; Loss History; ViolationHistory; Insurance Score; Credit Score; Homeownership; Home PolicyCurrently in Force; Vehicle Garage Location; Base Address Validation;Business or Commercial Address; Title History of Vehicle; Validation ofCommercial or Business Usage; Current Coverage Verification; PriorCoverage Limits; List of All Vehicles Owned By Household LicensedOperators; Capture of All Licensed Drivers in a Household; Ability toParse Out Apartment and Multiple-Dwelling Unit Hits; and Validation ofAnnual Mileage Driven

Potential Predictive Variables can include, but are not limited to:Military Status; Loss History; Violation History; Insurance Score;Homeownership; Business or Commercial Address; Title History of Vehicle;Indicators of Prior Damage; Validation of Commercial or Business Usage;Current Coverage Verification; Prior Coverage Limits; Insuring Company;Capture of All Licensed Vehicle Operators in a Household; and Validationof Annual Mileage

This has been a detailed description of some exemplary embodiments ofthe present disclosure contained within the disclosed subject matter.The detailed description refers to the accompanying drawings that form apart hereof and which show by way of illustration, but not oflimitation, some specific embodiments of the present disclosure,including a preferred embodiment. These embodiments are described insufficient detail to enable those of ordinary skill in the art tounderstand and implement the present disclosure. Other embodiments maybe utilized and changes may be made without departing from the scope ofthe present disclosure. Thus, although specific embodiments have beenillustrated and described herein, any arrangement calculated to achievethe same purpose may be substituted for the specific embodiments shown.This disclosure is intended to cover any and all adaptations orvariations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,will be apparent to those of skill in the art upon reviewing the abovedescription.

In the foregoing Detailed Description, various features are groupedtogether in a single embodiment for the purposes of streamlining thedisclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, the present disclosure lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate preferred embodiment. It will bereadily understood to those skilled in the art that various otherchanges in the details, material, and arrangements of the parts andmethod stages which have been described and illustrated in order toexplain the nature of this disclosure may be made without departing fromthe principles and scope as expressed in the subjoined claims.

What is claimed is:
 1. A computer system for processing and evaluatingapplication data for insurance, comprising: a memory configured to storeinstructions; a processor disposed in communication with said memory,wherein said processor upon execution of the instructions is configuredto: receive application data for an insurance policy regarding anapplicant said application data including a plurality of variables andwherein the plurality of variables comprises individual risk and ratevariables; determine which of the risk and rate variables are mostrelevant to the insurance policy; assign greater weight to the risk andrate variables most relevant to the insurance policy; validate theapplication data against data from at least a secondary data source;apply predicative analytics wherein said processor configured to applypredictive analytics includes said processor being further configuredto: analyze the application data and the data from said at least asecondary data source, modeling the application data and the data fromsaid at least a secondary data source using the plurality of variables,and extract information from both the application data and the data fromthe at least a secondary data source to predict future trends andbehavior patterns; determine a degree of confidence factor for each ofthe plurality of variables used by the model, wherein the degree ofconfidence factors estimates the reliability of the plurality ofvariables used by the model; calculate an aggregate confidence level byaggregating the degree of confidence factor for each of the plurality ofvariables, wherein the processor configured to calculate an aggregateconfidence level includes the processor being further configured to:weigh the degree of confidence factors for the risk and rate variablesin accordance with the greater weight assigned to the risk and ratevariables most relevant to the insurance policy; issue an insurancepolicy to the applicant when the aggregate confidence level is within apredetermined threshold for the aggregate confidence level; and indicatethat an insurance policy will not issue to the applicant when theaggregate confidence level is outside the predetermined threshold forthe aggregate confidence level.
 2. A non-transitory computer readablemedium comprising instructions, said instructions when executed by acomputer, cause the computer to perform a method for processing andevaluating application data for insurance, the method comprising:receiving application data for an insurance policy regarding anapplicant said application data including a plurality of variables andwherein the plurality of variables comprises individual risk and ratevariables; determining which of the risk and rate variables are mostrelevant to the insurance policy; assigning greater weight to the riskand rate variables most relevant to the insurance policy; validating theapplication data against data from at least a secondary data source;applying predicative analytics by: analyzing the application data andthe data from said at least a secondary data source, modeling theapplication data and the data from said at least a secondary data sourceusing the plurality of variables, and extracting information from boththe application data and the data from the at least a secondary datasource to predict future trends and behavior patterns; determining adegree of confidence factor for each of the plurality of variables usedby the model, wherein the degree of confidence factors estimates thereliability of the plurality of variables used by the model; calculatingan aggregate confidence level by aggregating the degree of confidencefactor for each of the plurality of variables wherein said step ofcalculating an aggregate confidence level further comprises: weighingthe degree of confidence factors for the risk and rate variables inaccordance with the greater weight assigned to the risk and ratevariables most relevant to the insurance policy; issuing an insurancepolicy to the applicant when the aggregate confidence level is within apredetermined threshold for the aggregate confidence level; andindicating that an insurance policy will not issue to the applicant whenthe aggregate confidence level is outside the predetermined thresholdfor the aggregate confidence level.
 3. A computer-implemented method forprocessing and evaluating application data for insurance, the methodcomprising the steps of: receiving, by a computer processor, applicationdata for an insurance policy regarding an applicant said applicationdata including a plurality of variables and wherein the plurality ofvariables comprises individual risk and rate variables; determiningwhich of the risk and rate variables are most relevant to the insurancepolicy; assigning greater weight to the risk and rate variables mostrelevant to the insurance policy; validating, by the computer processor,the application data against data from at least a secondary data source;applying, by the computer processor, predicative analytics by: analyzingthe application data and the data from said at least a secondary datasource, modeling the application data and the data from said at least asecondary data source using the plurality of variables, and extractinginformation from both the application data and the data from the atleast a secondary data source to predict future trends and behaviorpatterns; determining, by the computer processor, a degree of confidencefactor for each of the plurality of variables used by the model whereinthe degree of confidence factors estimates the reliability of theplurality of variables used by the model; calculating, by the computerprocessor, an aggregate confidence level by aggregating the degree ofconfidence factor for each of the plurality of variables, wherein saidstep of calculating an aggregate confidence level further comprises:weighting the degree of confidence factors for the risk and ratevariables in accordance with the greater weight assigned to the risk andrate variables most relevant to the insurance policy; issuing, by thecomputer processor, an insurance policy to the applicant when theaggregate confidence level is within a predetermined threshold for theaggregate confidence level; and indicating, by the computer processor,that an insurance policy will not issue to the applicant when theaggregate confidence level is outside the predetermined threshold forthe aggregate confidence level.
 4. The method of claim 3, furthercomprising providing to the applicant a rate quote for the insurancepolicy.
 5. The method of claim 4, further comprising prior to said stepof providing to the applicant a rate quote for the insurance policy,determining the threshold for the aggregate confidence level needed toissue the insurance policy to the applicant.
 6. The method of claim 3,wherein said step of modeling the application data and the data fromsaid at least one secondary data source using the plurality of variablesfurther comprises: flagging conflicts in the application data whencompared to the data from the at least a secondary data source.
 7. Themethod of claim 3, wherein the data from the at least a secondary datasource is historical data specific to the applicant.
 8. The method ofclaim 3, wherein the data from the at least a secondary data source isthird-party data.